Abstract: Queueing models that are used to capture various service settings typically assume that customers require a single unit of resources (servers) to be processed. However, there are many service settings where such an assumption may fail to capture the heterogeneity in resource requirements of different customers. For instance, clinical guidelines suggest that patients should be classified based on the level of medical attention/supervision required. We propose a multi-server queueing model with multiple customer classes in which customers from different classes may require different amounts of resources to be served. We study the optimal scheduling policy for such systems. To balance the holding cost, the service rate, the resource requirement, and the priority-induced idleness, we develop a class of index-based policies which we refer to as the idle-aware cμ/m rule. We establish the asymptotic optimality of this class of policies in the many-server heavy-traffic regime. For a two-class two-server model, where policy-induced idleness can have a big impact on system performance, we establish a uniform performance bound on the amount of sub-optimality incurred by the idle-avoid cμ/m rule (a special case of the idle-aware cμ/m rule). This theoretical bound, along with numerical experiments, provides support for the robustness of our proposed class of policies.

Bio: Noa Zychlinski completed her PhD at the Technion in Israel under the supervision of Professor Avishai Mandelbaum and Dr. Izack Cohen. She is now a Postdoctoral Research Fellow in the Division of Decision, Risk, and Operations at Columbia Business School. Her research interests focus on service operations and management.

Noa is interested in the analysis of queueing networks and their applications, the theory of stochastic process approximation, and data analysis of large service systems. Her work focuses mainly on operational models that are motivated by healthcare systems, in which strategic and operational decisions can improve patient care, patient outcomes, shorten waiting times and reduce operational costs. Noa has applied these approaches to problems in scheduling and prioritizing patients, dynamic allocation of resources, and bed planning.